TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Adams, Tim
T1  - Automated Lung Tumor Diagnosis in Medical Image Data - Methods, Challenges and Perspectives
T2  - Klewitz-Hommelsen, Lang, Schönbach (Eds.): Science Track FrOSCon 2018
N2  - Cancer is one of the leading causes of death worldwide [183], with lung tumors being the most frequent cause of cancer deaths in men as well as one of the most common cancers diagnosed in woman [40]. As symptoms often arise in advanced stages, an early diagnosis is especially important to ensure the best and earliest possible treatment. In order to achieve this, Computed Tomography (CT) scans are frequently used for tumor detection and diagnosis. We will present examples of publicly available CT image data of lung cancer patients and discuss possible methods to realize an automatic system for automated cancer diagnosis. We will also look at the recent SPIE-AAPM Lung CT Challenge [10] data set in detail and describe possible methods and challenges for image segmentation and classification based on this data set.
UR  - https://doi.org/10.18418/978-3-96043-093-3
SN  - 978-3-96043-093-3
SB  - 978-3-96043-093-3
SP  - 13
EP  - 18
S1  - 6
PB  - Hochschule Bonn-Rhein-Sieg
CY  - Sankt Augustin
ER  -